Transportation and Activity Systems

Support:
U.S.
Department of Transportation and California Department of Transportation/University of California
Transportation Center

The work proposed here will be based on previous
activity-based research conducted by the principal investigator and his
colleagues and will be directed toward developing a practical planning application
of a mathematical programming activity-based model as an effective travel
demand forecasting tool.

In this proposed
research, we seek to complete the modeling framework that has evolved over past
research efforts by extending it to a "traditional" planning
framework. Specifically, we will couch the activity-based approach in terms
that are amenable to its development as a planning tool for travel demand
forecasting that not only provides output consistent with accepted trip-based
static planning methodologies, but further provides full estimates of the
associated dynamics of trip generation, distribution and route selection; all
from a theoretically consistent paradigm based on the need/desire of households
to interact with their environment. By showing that the particular mathematical
programming paradigm can be used to describe the demand modeling processes both
for conventional trip-based travel demand and for activity- based approaches it
is hoped not only to facilitate the practicality of activity-based modeling
approaches, but also to tap into the wealth of research that has guided
mainstream travel demand analysis.

If successful, the research will produce the first known activity-based model
framework that can be applied to empirical demand forecasting -an application
that has long eluded activity-based modeling proponents.

Assessing the Influence
of Residential Location Changes on Travel Behavior

Support: U.S. Department of Transportation and California
Department of Transportation/University of California Transportation Center

There are certain
fundamental transportation problems that have remained problems, in part, due
to an inability to effectively collect the data necessary to address the
problem. One such problem involves the "learning" process by which a
household re-locating into a new neighborhood evolves new household activity
patterns. More specifically, when a household relocates, what are the immediate
and longer term impacts on travel behavior of the local activity and
transportation systems? How do household travel patterns evolve? While simple
logic suggests that new alternatives will be available for travel and activity
decision-making, what are these choices and how does knowledge of these choices
evolve.

This project proposes to
use technologies developed in prior UCTC, PATH, and Testbed research projects
to facilitate the observation of a small number of households re-locating from
other areas in Orange
County, CA to selected new home
developments in Irvine. We will install
in-vehicle GPS/Wireless Communication units in all household vehicles to
measure specific vehicle use for a multi-day period prior to moving, upon
re-locating, and a few months after relocating to Irvine. We will also have the
sampled households use iCHASE, an computer-based survey research software
developed in prior UCTC research, to record their household activities during
this same period. We will utilize GIS-based data sets depicting both the local
activity-systems and transport networks. Together, this data will enable us to
address the immediate changes in travel behavior upon relocation, and to assess
the evolution of stability in this behavior over time.

A
Household Survey via an Internet GIS for Models of Activity Scheduling

Investigator:
Ming-Sheng Lee

Support:
U.S.
Department of Transportation and California Department of Transportation/ University of California
Transportation Center

This project uses data from a geographic information
system (GIS)-based household survey on the Internet to build a production
system model of household activity scheduling. The model is a rule-based system
that shows how activities are initially scheduled and dynamically changed
during execution. Transactional opportunistic problem solving are being used to
simulate dynamic scheduling behavior. The model is being verified by comparing
model outputs to activity patterns recorded in the existing activity/travel
diaries.

Despite the importance of assessing the reliability of
road networks, there exist only a few suitable techniques. The approaches used
in water supply systems, communication systems, and power transmission systems
are not directly applicable for transportation systems. The reason is that
these approaches ignore route choice behavior when evaluating the performance
reliability of a network. This research proposes to incorporate a risk-taking,
route choice behavior when estimating travel time reliability of a road
network. The proposed research approach will allow the evaluation of network
performance under uncertainty. It is particularly useful for the traffic
information systems in which travel time information (not only the mean travel
time but also the variance of travel time) is provided to the network users for
decision-making. It is anticipated that the proposed research can also be used
to evaluate the performance of the Advanced Traveler Information Systems (ATIS)
and to improve the level-of-service of a road network.

A significant part of this project focuses on
developing realistic route choice models by incorporating both the traveler's imperfect
knowledge of network travel times as well as the variability of these travel
times. Since the route choice problem and the origin-destination (OD)
estimation problem are inter-dependent (i.e., the input of one problem is the
output of the other problem), this project is also expected to contribute to
improving the accuracy and reliability of the OD estimation problem. Though the
OD estimation problem is not explicitly addressed in this proposal, the
development of realistic route choice models is a key factor in the
investigation of origin-destination demand procedures. In addition, it is
possible to assess the quality of the estimated OD demand using the
variance-covariance matrices of the link choice proportions resulted from the
proposed route choice models since uncertainties associated with supply and
demand variations are explicitly captured in the models.

The OCTA RFP summarizes the state-of-the-practice for
macro-simulation modeling in Orange County. The Orange County Transportation Authority
(Authority) is responsible for transportation modeling in Orange County. The
current Orange County Transportation Analysis model (OCTAM) has been recently
updated to OCTAM 3.0 for Base Year 1998 and further improvements are on-going.
OCTAM 3.0 incorporates state-of-the-practice modeling components that are
consistent with the new Southern California Regional Transportation Model
recently released by the Southern California Association of Governments (SCAG).
The OCTAM 3.0 model provides a regional travel forecasting base for
transportation planning work in Orange County. The OCTAM model is regional in nature and suited for
macro-level analysis.

This proposal offers professional services, which are
required to assist the regional modeling section in evaluating and integrating
a traffic micro-simulation model. The purpose of integrating a micro-simulation
with the macro-simulation OCT AM model is to allow a more detailed evaluation
of projects through the analysis of various measures of effectiveness. OCTA has
identified candidate projects for the application of the micro- simulation
model, including network infrastructure improvements such as gap closures,
arterial widening, and the addition of HOV or general purpose lanes, as well as
operational enhancements such as grade separation of rail, freeway auxiliary
lane addition, ramp metering, one-way couplets, reversible lanes, reconfiguration
of interchanges, smart streets, and the identification of specific locations to
target for improvements such as arterial or freeway bottlenecks.

Support:
U.S. Department of
Transportation and California Department of Transportation/ University of California Transportation Center

This project is the
second phase of a planned two-year research effort. The primary goal of the
research is a fundamental examination of the behavioral process that results in
revealed travel behavior. To reveal this process, a computer-based household
activity survey program, CHASE, is being re-programmed, enhanced, and extended
for Internet application (iCHASE), integrated with a GIS, and utilized in a
pilot study to collect data for a study of the determinants of travel and
activity behavior in households. These data are inherently dynamic, since
CHASE respondents record planned activity agendas and then update and schedule
these agenda, on a daily basis, fully defined in time and space (with CHASE
recording the process of adding, modifying, and deleting components of a weekly
travel pattern). The resultant data will facilitate the identification of
fundamental inter-relationships among a comprehensive range of revealed travel
and activity participation variables, leading toward the identification of what
are the critical variables, relationships, and rules that govern that behavior.
It is believed that an internet-based travel survey, particularly one as rich
in resultant content as ICHASE, will significant reduce data collection costs,
improve data quality and quantity, and allow for continuous data collection.

Understanding total residential transportation energy
usage is vital for the planning of conservation measures and the evaluation of
incentives and mandates aimed at vehicle fuel efficiency. The annual fuel
consumption of households is the outcome of complex decisions that involve the
number of vehicles the household owns, leases or has other access to (including
company cars), the makes, models and vintages of these vehicles, allocation of
vehicles among drivers, allocation of activities to drivers and non-drivers,
and choices of mode and activity site. Lifestyle plays a major role. So do
demographic and socioeconomic factors, including age, race, ethnicity,
education, and income. Spatial location of residence and workplace determines
trip lengths, travel opportunities, and the availability of public
transportation and non-motorized modes. The proposed research uses the 2001
National Household Transportation Survey that has just become available (in
January 2003) to model fuel consumption by California households as a function of the types of vehicles in
the household fleet, demographic and socioeconomic characteristics, and
residential location. Vehicle types can be defined by any combination of
vehicle, make, model and vintage, but it is expected that it will be
instructive to use both a consumer-based typology (e.g., subcompact cars,
compact cars, compact pickups, full-sized trucks, minivans, compact SUVs, fullsize
SUVs) and a fuel-efficiency breakdown for classification purposes. Conclusions
will be drawn regarding potential changes in fuel consumption resulting from
planned residential developments in California, as well as expected effects of changes in new
vehicle fleet fuel-efficiency. Forecasts will be made of the fuel savings that
are likely to result from different levels of penetration, by market segment,
of new fuel-efficient and alternative-fuel vehicles, including hybrid fuel-cell
vehicles.